Data Scientist

Piper Companies; Durham (NC); Hybrid; USD 115,000 - 135,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Paid Time Off

Parental leave

401(k) with company match

Job summary

Piper Companies is seeking a Data Scientist for a hybrid permanent role in Durham, NC. The role focuses on transforming healthcare data into research-ready datasets and conducting statistical analyses to support evidence generation and decision-making.

The ideal candidate will have a Master’s in a quantitative field, strong R skills, and experience with healthcare data and observational studies. Benefits include comprehensive coverage and a hybrid work arrangement.

Qualifications

• Master's degree in Data Science, Biostatistics, Epidemiology, or related quantitative field.
• Strong proficiency with R programming and reproducible code.
• Experience with relational databases and SQL queries.
• Experience with healthcare datasets or observational studies preferred.

Responsibilities

• Develop and maintain scalable R-based pipelines for transforming claims and EHR data into research-ready datasets.
• Query and manipulate large relational databases using SQL and R tools with study criteria.
• Perform statistical analyses, model diagnostics, and contribute to causal inference and comparative effectiveness projects.
• Generate tables and figures for client reports and scientific deliverables.
• Collaborate in code reviews and QC with statisticians, epidemiologists, and researchers.

Skills

R programming

SQL

dbplyr

Education

Master's degree in Data Science or related field

Tools

Relational databases

Job description

Piper Companies is seeking a Data Scientist to join a growing healthcare analytics and life sciences consulting organization for a hybrid permanent position based out of Durham, NC. The Data Scientist will support observational research initiatives by transforming complex healthcare data into research-ready datasets, executing statistical analyses, and contributing to the development of reproducible analytical workflows that support evidence generation and healthcare decision-making.

Responsibilities of the Data Scientist include:

• Develop and maintain scalable R-based analytical pipelines that convert healthcare claims and EHR data into research-ready datasets for observational studies.
• Query and manipulate large relational databases using SQL and R-based tools while applying study-specific inclusion, exclusion, and follow-up criteria.
• Execute statistical analyses, perform model diagnostics, and assist with causal inference and comparative effectiveness research projects.
• Generate tables, figures, and research outputs for client-facing reports and scientific deliverables.
• Participate in code reviews, quality control processes, and cross-functional collaboration with statisticians, epidemiologists, and other research stakeholders.

Qualifications for the Data Scientist include:

• Master's degree in Data Science, Biostatistics, Epidemiology, Health Economics, Bioinformatics, or a related quantitative discipline; equivalent professional experience considered.
• Strong proficiency with R programming and experience developing clean, reproducible, and well-documented code.
• Experience working with relational databases and writing SQL queries or leveraging database interfaces such such as dbplyr.
• Ability to communicate technical findings effectively and collaborate within multidisciplinary scientific teams.
• Experience with healthcare datasets, longitudinal research, real-world evidence, or observational studies is highly preferred.

Compensation for the Data Scientist:

Salary Range: $115,000-$135,000/year (USD)

Comprehensive Benefits: Medical, Dental, Vision, Paid Time Off, Paid Parental Leave, 401(k) with company match, and additional employee benefits.

Keywords: data scientist, healthcare analytics, real world evidence, R programming, SQL, dbplyr, epidemiology, biostatistics, observational research, comparative effectiveness, causal inference, healthcare data, electronic health records, EHR, claims data, longitudinal data, statistical modeling, analytics pipeline, data engineering, reproducible research, GitLab, data visualization, reporting, healthcare outcomes, health economics, regulatory research, patient cohorts, propensity scores, quality control, life sciences, data management, healthcare research, statistical analysis, clinical analytics, database querying, research methods, predictive analytics, evidence generation.

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